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Survival Analysis of Coal Workers’ Pneumoconiosis (CWP) Patients in a State-Owned Mine in the East of China from 1963 to 2014

Author

Listed:
  • Lei Han

    (Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210028, China
    Department of Occupational Medicine and Environmental Health, School of Public Health, Nanjing Medical University, Nanjing 210029, China
    These authors contributed equally to this work.)

  • Qianqian Gao

    (Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210028, China
    These authors contributed equally to this work.)

  • Jingjin Yang

    (Department of Occupational Medicine and Environmental Health, School of Public Health, Nanjing Medical University, Nanjing 210029, China
    Division of Health Risk Factor Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
    These authors contributed equally to this work.)

  • Qiuyun Wu

    (Department of Occupational Medicine and Environmental Health, School of Public Health, Nanjing Medical University, Nanjing 210029, China)

  • Baoli Zhu

    (Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210028, China)

  • Hengdong Zhang

    (Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210028, China)

  • Bangmei Ding

    (Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210028, China)

  • Chunhui Ni

    (Department of Occupational Medicine and Environmental Health, School of Public Health, Nanjing Medical University, Nanjing 210029, China)

Abstract

To investigate the mortality probability, life expectancy of coal workers’ pneumoconiosis (CWP), and related factors of life expectancy, a total of 495 patients with CWP were diagnosed and reported from 1963 to 2014 in a state-owned mine in the east of China. The life table method, log rank method, and Cox regression model were used for survival analysis. 95 out of 495 CWP died during this period. The mortality rate was 19.19%. The average life span was 12.1 (0.0–33.2) years and average death age was 57.4 (33.0–83.0) years. The life table indicated that overall mortality probability increased with the age of CWP patients. Life expectancy of CWP patients was prolonged to 4.3, 1.4, 1.2, and 1.4 years without death caused by pneumoconiosis, tuberculosis, lung cancer, and pulmonary heart disease respectively. The survival curve of CWP patients without pulmonary tuberculosis was higher (average 37.9 years) than patients with pulmonary tuberculosis (average 34.1 years). There was significant difference observed (χ 2 = 6.196, p < 0.05). Three risk factors that include initial dust exposure year, age of onset, and first diagnostic stage were put into the Cox regression model for evaluation. The data indicated that prevention and treatment of CWP complication is important to improve patients’ survival rates.

Suggested Citation

  • Lei Han & Qianqian Gao & Jingjin Yang & Qiuyun Wu & Baoli Zhu & Hengdong Zhang & Bangmei Ding & Chunhui Ni, 2017. "Survival Analysis of Coal Workers’ Pneumoconiosis (CWP) Patients in a State-Owned Mine in the East of China from 1963 to 2014," IJERPH, MDPI, vol. 14(5), pages 1-10, May.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:5:p:489-:d:97744
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    References listed on IDEAS

    as
    1. Lei Han & Ruhui Han & Xiaoming Ji & Ting Wang & Jingjin Yang & Jiali Yuan & Qiuyun Wu & Baoli Zhu & Hengdong Zhang & Bangmei Ding & Chunhui Ni, 2015. "Prevalence Characteristics of Coal Workers’ Pneumoconiosis (CWP) in a State-Owned Mine in Eastern China," IJERPH, MDPI, vol. 12(7), pages 1-12, July.
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    Cited by:

    1. Liton Devnath & Peter Summons & Suhuai Luo & Dadong Wang & Kamran Shaukat & Ibrahim A. Hameed & Hanan Aljuaid, 2022. "Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review," IJERPH, MDPI, vol. 19(11), pages 1-22, May.

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